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The application of artificial neural networks to the analysis of remotely sensed data / J.F. Mas in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 617 - 663 Titre : The application of artificial neural networks to the analysis of remotely sensed data Type de document : Article/Communication Auteurs : J.F. Mas, Auteur ; J.J. Flores, Auteur Année de publication : 2008 Article en page(s) : pp 617 - 663 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal [Termes IGN] image aérienne [Termes IGN] image satellite [Termes IGN] réseau neuronal artificiel Résumé : (Auteur) Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis. Copyright Taylor & Francis Numéro de notice : A2008-004 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701352154 En ligne : https://doi.org/10.1080/01431160701352154 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28999 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Comparison and improvement of wavelet-based image fusion / G. Hong in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 673 - 691 Titre : Comparison and improvement of wavelet-based image fusion Type de document : Article/Communication Auteurs : G. Hong, Auteur ; Y. Zhang, Auteur Année de publication : 2008 Article en page(s) : pp 673 - 691 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse en composantes principales [Termes IGN] fusion d'images [Termes IGN] image Ikonos [Termes IGN] image Quickbird [Termes IGN] ondelette [Termes IGN] transformation en ondelettes [Termes IGN] transformation intensité-teinte-saturation Résumé : (Auteur) The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non-orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image-fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet-based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the 'à trous', wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet-integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non-orthogonal), decimation or undecimation, and wavelet-decomposition levels. Copyright Taylor & Francis Numéro de notice : A2008-005 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701313826 En ligne : https://doi.org/10.1080/01431160701313826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29000 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Phase unwrapping for SAR interferometry based on an ant colony optimization algorithm / Z.Q. Wei in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 711 - 725 Titre : Phase unwrapping for SAR interferometry based on an ant colony optimization algorithm Type de document : Article/Communication Auteurs : Z.Q. Wei, Auteur ; F. Xu, Auteur ; Ya-Qiu Jin, Auteur Année de publication : 2008 Article en page(s) : pp 711 - 725 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] modèle numérique de surface [Termes IGN] optimisation par colonie de fourmis [Termes IGN] résidu Résumé : (Auteur) Phase unwrapping is a key step in extracting digital elevation models (DEMs) from interferometric synthetic aperture radar (InSAR) data. A new two-dimensional (2-D) phase unwrapping algorithm based on ant colony optimization (ACO) is proposed, which is used to configure the shortest path linking all residues in an interferogram. Using an optimization strategy to establish the branch cuts, the unwrapping error can be significantly reduced. Simulated and real InSAR image datasets are studied to evaluate the performance of the algorithm. The results of the simulated datasets show that the errors of the algorithm are lower than some conventional methods, and the results from a real InSAR image dataset demonstrate that the ACO approach has no isolated regions in comparison with some conventional approaches. It indicates that our ACO algorithm is an optional compromise strategy between preferable phase unwrapping precision and time-consuming computation.Copyright Taylor & Francis Numéro de notice : A2008-006 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701281049 En ligne : https://doi.org/10.1080/01431160701281049 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29001 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Improved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode / S.A. Soenen in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1007 - 1027 Titre : Improved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode Type de document : Article/Communication Auteurs : S.A. Soenen, Auteur ; Derek R. Peddle, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 1007 - 1027 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alberta (Canada) [Termes IGN] canopée [Termes IGN] classe d'objets [Termes IGN] correction du signal [Termes IGN] forêt tempérée [Termes IGN] image SPOT [Termes IGN] modélisation 3D [Termes IGN] Pinus (genre) [Termes IGN] précision de la classification [Termes IGN] réflectance végétale [Termes IGN] variance Résumé : (Auteur) In most forestry remote sensing applications in steep terrain, simple photometric and empirical (PE) topographic corrections are confounded as a result of stand structure and species assemblages that vary with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we present MFM-TOPO as a new physically-based modelling (PBM) approach for normalising topographically induced signal variance as a function of forest stand structure and sub-pixel scale components. MFM-TOPO uses the Li-Strahler geometric optical mutual shadowing (GOMS) canopy reflectance model in Multiple Forward Mode (MFM) to account for slope and aspect influences directly. MFM-TOPO has an explicit physical-basis and uses sun-canopy-sensor (SCS) geometry that is more appropriate than strictly terrain-based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. MFM-TOPO is compared against our recently developed SCS+C correction and a comprehensive set of other existing PE and SCS methods (cosine, C correction, Minnaert, statistical-empirical, SCS, and b correction) for removing topographically induced variance and for improving SPOT image classification accuracy in a Rocky Mountain forest in Kananaskis, Alberta Canada. MFM-TOPO removed the most terrain-based variance and provided the greatest improvement in classification accuracy within a species and stand density based class structure. For example, pine class accuracy was increased by 62% over shaded slopes, and spruce class accuracy was increased by 13% over more moderate slopes. In addition to classification, MFM-TOPO is suitable for retrieving biophysical parameters in mountainous terrain. Numéro de notice : A2008-007 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701311291 En ligne : https://doi.org/10.1080/01431160701311291 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29002 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Land-cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling / F. Yuan in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1169 - 1184 Titre : Land-cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling Type de document : Article/Communication Auteurs : F. Yuan, Auteur Année de publication : 2008 Article en page(s) : pp 1169 - 1184 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aménagement du territoire [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] carte d'occupation du sol [Termes IGN] détection de changement [Termes IGN] fusion d'images [Termes IGN] image Quickbird [Termes IGN] impact sur l'environnement [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] photographie aérienne [Termes IGN] planification urbaine [Termes IGN] surface imperméable [Termes IGN] système d'information géographique [Termes IGN] urbanisation Résumé : (Auteur) Land use and land-cover (LULC) data provide essential information for environmental management and planning. This research evaluates the land-cover change dynamics and their effects for the Greater Mankato Area of Minnesota using image classification and Geographic Information Systems (GIS) modelling in high-resolution aerial photography and QuickBird imagery. Results show that from 1971 to 2003, urban impervious surfaces increased from 18.3% to 32.6%, while cropland and grassland decreased from 54.2% to 39.1%. The dramatic urbanization caused evident environmental impacts in terms of runoff and water quality, whereas the annual air pollution removal rate and carbon storage/sequestration remained consistent since urban forests were steady over the 32-year span. The results also indicate that highly accurate land-cover features can be extracted effectively from high-resolution imagery by incorporating both spectral and spatial information, applying an image-fusion technique, and utilizing the hierarchical machine-learning Feature Analyst classifier. This research fills the high-resolution LULC data gap for the Greater Mankato Area. The findings of the study also provide valuable inputs for local decision-makers and urban planners. Copyright Taylor & Francis Numéro de notice : A2008-008 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701294703 En ligne : https://doi.org/10.1080/01431160701294703 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29003 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Multispectral land use classification using neural networks and support vector machines: one or the other, or both? / B. Dixon in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1185 - 1206 Titre : Multispectral land use classification using neural networks and support vector machines: one or the other, or both? Type de document : Article/Communication Auteurs : B. Dixon, Auteur ; N. Candade, Auteur Année de publication : 2008 Article en page(s) : pp 1185 - 1206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] occupation du sol Résumé : (Auteur) Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote-sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote-sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less. Copyright Taylor & Francis Numéro de notice : A2008-009 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701294661 En ligne : https://doi.org/10.1080/01431160701294661 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29004 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible